This is the R project of Fabian and Robin. We decided to analyse data from billionaires.
For that, we found a dataset on kaggle.com: https://www.kaggle.com/datasets/nelgiriyewithana/billionaires-statistics-dataset
This dataset contains data from the 4th of april 2023 and is used for ‘exploring the global landscape of success’.

In the following, we will create several graphs to compare the correlation between certain columns of data and wealth. As this dataset only contains billionaires, we won’t focus on the difference between their wealth. Being part of this list already means being incredibly rich. Accordingly, it is not necessary to analyze how much wealth is considered rich.

1. Definition and formulation of the questions

What interests you about the data set? (Our theses)

  • The distribution of billionaire wealth is highly concentrated among a small number of individuals.
  • There is a strong correlation between the industries in which billionaires make their fortunes and the overall GDP of their countries.
  • The geographic distribution of billionaires across countries reflects the global economic landscape and the opportunities for wealth creation in different regions.
  • The relationship between age and becoming a billionaire.
  • Countries with lower tax rates have more billionaires.
  • Countries with better educational attainment have more billionaires.

What specific questions would you like to answer with the help of the data set?

  • How many billionaires own more than 80% of the total wealth?
  • Which industry is the most profitable?
  • What is the relationship between the distribution of billionaire wealth and the economic strength of a country, measured by GDP, tax revenue and population?
  • Is there a geographical pattern to the distribution of billionaires across countries?
    • If so, what factors contribute to this distribution?
  • How many billionaires are self-made, and are younger billionaires more likely to be self-made?
  • In which countries do most billionaires inherit their wealth and why?
  • Which countries have the lowest tax rates and how many billionaires do they have?
  • Is there a correlation between educational attainment and the number of billionaires in a country?

What do you expect from the data set in view of your question?

  • Most wealth is held by 10% of billionaires.
  • The most profitable sector is technology.
  • Modern economies have more self-made billionaires.
  • There is no geographical pattern.
  • Younger billionaires are more likely to be self-made.
  • Location does not correlate with wealth.

2. Loading the data

Load the data into the R session and get an initial overview Which types are included?

Describe what you need to do before you can prepare and edit the data in the next section!

Load Libraries

# Installation of the needed libraries, if not already installed
if (!require(ggplot2)) install.packages("ggplot2")
if (!require(dplyr)) install.packages("dplyr")
if (!require(ggcorrplot)) install.packages("ggcorrplot")
if (!require(ggthemes)) install.packages("ggthemes")
if (!require(tidyverse)) install.packages("tidyverse")
if (!require(gridExtra)) install.packages("gridExtra")
if (!require(plotly)) install.packages("plotly")

# We used these libraries
library(ggplot2)
library(dplyr)
library(ggcorrplot)
library(ggthemes)
library(tidyverse)
library(gridExtra)
library(plotly)

Read the CSV file into a dataframe called df_billionaires

df_billionaires <- read.csv("BillionairesStatisticsDataset.csv")
# display data
head(df_billionaires)
##   rank finalWorth              category               personName age       country   city             source
## 1    1     211000      Fashion & Retail Bernard Arnault & family  74        France  Paris               LVMH
## 2    2     180000            Automotive                Elon Musk  51 United States Austin      Tesla, SpaceX
## 3    3     114000            Technology               Jeff Bezos  59 United States Medina             Amazon
## 4    4     107000            Technology            Larry Ellison  78 United States  Lanai             Oracle
## 5    5     106000 Finance & Investments           Warren Buffett  92 United States  Omaha Berkshire Hathaway
## 6    6     104000            Technology               Bill Gates  67 United States Medina          Microsoft
##              industries countryOfCitizenship                     organization selfMade status gender       birthDate
## 1      Fashion & Retail               France LVMH Moët Hennessy Louis Vuitton    FALSE      U      M   3/5/1949 0:00
## 2            Automotive        United States                            Tesla     TRUE      D      M  6/28/1971 0:00
## 3            Technology        United States                           Amazon     TRUE      D      M  1/12/1964 0:00
## 4            Technology        United States                           Oracle     TRUE      U      M  8/17/1944 0:00
## 5 Finance & Investments        United States   Berkshire Hathaway Inc. (Cl A)     TRUE      D      M  8/30/1930 0:00
## 6            Technology        United States  Bill & Melinda Gates Foundation     TRUE      D      M 10/28/1955 0:00
##   lastName firstName                title          date      state residenceStateRegion birthYear birthMonth birthDay
## 1  Arnault   Bernard     Chairman and CEO 4/4/2023 5:01                                      1949          3        5
## 2     Musk      Elon                  CEO 4/4/2023 5:01      Texas                South      1971          6       28
## 3    Bezos      Jeff Chairman and Founder 4/4/2023 5:01 Washington                 West      1964          1       12
## 4  Ellison     Larry      CTO and Founder 4/4/2023 5:01     Hawaii                 West      1944          8       17
## 5  Buffett    Warren                  CEO 4/4/2023 5:01   Nebraska              Midwest      1930          8       30
## 6    Gates      Bill              Cochair 4/4/2023 5:01 Washington                 West      1955         10       28
##   cpi_country cpi_change_country          gdp_country gross_tertiary_education_enrollment
## 1      110.05                1.1  $2,715,518,274,227                                 65.6
## 2      117.24                7.5 $21,427,700,000,000                                 88.2
## 3      117.24                7.5 $21,427,700,000,000                                 88.2
## 4      117.24                7.5 $21,427,700,000,000                                 88.2
## 5      117.24                7.5 $21,427,700,000,000                                 88.2
## 6      117.24                7.5 $21,427,700,000,000                                 88.2
##   gross_primary_education_enrollment_country life_expectancy_country tax_revenue_country_country total_tax_rate_country
## 1                                      102.5                    82.5                        24.2                   60.7
## 2                                      101.8                    78.5                         9.6                   36.6
## 3                                      101.8                    78.5                         9.6                   36.6
## 4                                      101.8                    78.5                         9.6                   36.6
## 5                                      101.8                    78.5                         9.6                   36.6
## 6                                      101.8                    78.5                         9.6                   36.6
##   population_country latitude_country longitude_country
## 1           67059887         46.22764          2.213749
## 2          328239523         37.09024        -95.712891
## 3          328239523         37.09024        -95.712891
## 4          328239523         37.09024        -95.712891
## 5          328239523         37.09024        -95.712891
## 6          328239523         37.09024        -95.712891

First inspection of the data

Each row displays one person that is a billionaire. The columns contain the following
details about each person:

  • rank
    • The ranking of the billionaire in terms of wealth (from 1 to 2640).
  • finalWorth
    • The final net worth of the billionaire in U.S. dollars.
  • category
    • The category or industry in which the billionaire’s business operates.
  • personName
    • The full name of the billionaire.
  • age
    • The age of the billionaire in years.
  • country
    • The country in which the billionaire resides.
  • city
    • The city in which the billionaire resides
  • source
    • The source of the billionaire’s wealth (eg. company name).
  • industries
    • The industries associated with the billionaire’s business interests.
  • countryOfCitizenship
    • The country of citizenship of the billionaire.
  • organization
    • The name of the organization or company associated with the billionaire.
  • selfMade
    • Indicates whether the billionaire is self-made (True/False).
  • status
    • D=Entrepreneur and U = Inherited
  • gender
    • The gender of the billionaire.
  • birthDate
    • The birthdate of the billionaire.
  • lastName
    • The last name of the billionaire.
  • firstName
    • The first name of the billionaire.
  • title
    • The status or position of the billionaire (e.g., CEO, Founder).
  • date
    • The date of data collection.
  • state
    • The state in which the billionaire resides.
  • redidenceStateRegion
    • The region or state of residence of the billionaire.
  • birthYear
    • The birth year of the billionaire.
  • birthMonth
    • The birth month of the billionaire.
  • birthDay
    • The birth day of the billionaire.
  • cpi_country
    • Consumer Price Index (CPI) for the billionaire’s country.
  • cpi_change_country
    • CPI change for the billionaire’s country.
  • gdp_country
    • Gross Domestic Product (GDP) for the billionaire’s country.
  • gross_tertiary_education_enrollment
    • Enrollment in tertiary education in the billionaire’s country.
  • gross_primary_education_enrollment_country
    • Enrollment in primary education in the billionaire’s country.
  • life_expectancy_country
    • Life expectancy in the billionaire’s country.
  • tax_revenue_country_country
    • Tax revenue in the billionaire’s country.
  • total_tax_rate_country
    • Total tax rate in the billionaire’s country.
  • population_country
    • Population of the billionaire’s country.
  • latitude_country
    • Latitude coordinate of the billionaire’s country.
  • longitude_country
    • Longitude coordinate of the billionaire’s country.

We first started by creating a list of the top 10 richest billionaires:

The plot displays ten bars in a barchart. Each bar represents the wealth of one billionaire. The plot only displays the top 10 richest billionaires. The height of the bar represents the wealth of the billionaire in US-dollars.

correlation matrix between numeric values

The code calculates and visualizes the correlation matrix of numeric variables in the dataset, indicating the strength and direction of linear relationships between measures such as wealth, age, Consumer Price Index, Gross Domestic Product, life expectancy, and other relevant numeric columns.

3. Editing/transforming the data

In this section, you should perform all the necessary transformations/cleansing/… etc. of the data (Data Muning, Data Cleansing), e.g:

Get an overview of the transformed data. You can use tools such as glimpse(), skim() and head() to illustrate your explanations.

Is the resulting data what you expected? Why or why not?

The distribution of billionaire wealth is highly concentrated among a small number of individuals.

# Calculate the total wealth
total_wealth <- sum(df_billionaires$finalWorth)

# Calculate percentage of billionaires wealth compared to global wealth:
result <- (total_wealth * 1e6) / (454.4 * 1e12)

# Sort billionaires by wealth in descending order
sorted_billionaires <- df_billionaires[order(df_billionaires$finalWorth, decreasing = TRUE), ]

# Calculate the cumulative sum of wealth
sorted_billionaires$cumulative_wealth <- cumsum(sorted_billionaires$finalWorth)

# Find the number of billionaires required to own more than 80% of total wealth
num_billionaires_80_percent <- sum(sorted_billionaires$cumulative_wealth <= 0.8 * total_wealth)

# Find the number of billionaires required to own more than 80% of total wealth
num_billionaires_50_percent <- sum(sorted_billionaires$cumulative_wealth <= 0.5 * total_wealth)

There is a strong correlation between the industries in which billionaires make their fortunes and the overall GDP of their countries.

# Clean 'finalWorth' column and convert to numeric
df_billionaires$finalWorth <- as.numeric(gsub("[^0-9.]", "", df_billionaires$finalWorth))

# Filter the dataset for billionaires with wealth below $25,000
wealth_filtered <- subset(df_billionaires, finalWorth < 25000)

# Calculate average wealth by industry
average_wealth_by_industry <- tapply(df_billionaires$finalWorth, df_billionaires$industries, mean, na.rm = TRUE)

# Identify the industry with the highest average wealth
most_profitable_industry <- names(average_wealth_by_industry[which.max(average_wealth_by_industry)])

# Create a data frame for plotting
industry_plot_data <- data.frame(
    industry = names(average_wealth_by_industry),
    average_wealth = average_wealth_by_industry
)

# Sort the data frame by average wealth
industry_plot_data <- industry_plot_data[order(industry_plot_data$average_wealth, decreasing = TRUE), ]

# Convert columns with monetary values to numeric
gdp_wealth_data <- df_billionaires
gdp_wealth_data$finalWorth <- as.numeric(gsub("[^0-9.]", "", gdp_wealth_data$finalWorth))
gdp_wealth_data$gdp_country <- as.numeric(gsub("[^0-9.]", "", gdp_wealth_data$gdp_country))
gdp_wealth_data$tax_revenue_country_country <- as.numeric(gsub("[^0-9.]", "", gdp_wealth_data$tax_revenue_country_country))
gdp_wealth_data$population_country <- as.numeric(gsub("[^0-9.]", "", gdp_wealth_data$population_country))

# Filter out rows with missing values in relevant columns
gdp_wealth_data <- gdp_wealth_data %>% filter(!is.na(finalWorth) & !is.na(gdp_country))

The geographic distribution of billionaires across countries reflects the global economic landscape and the opportunities for wealth creation in different regions.

# Filter for billionaires with country
df_geographical <- na.omit(df_billionaires["country"]) %>%
    # Group by country
    group_by(country) %>%
    # Count billionaires per country
    summarise(count = n()) %>%
    # Sort by count
    arrange(desc(count))

# Get finalworth per country
df_worth_country <- na.omit(df_billionaires[c("country", "finalWorth")]) %>%
    # Group by country
    group_by(country) %>%
    # Count billionaires per country
    summarise(
        count = n(),
        total_finalWorth = (sum(finalWorth) / 1000)
        # Hier können Sie weitere Aggregationsfunktionen hinzufügen, falls benötigt
    ) %>%
    # Keep only countries with more than 4 billionaires
    filter(count >= 5)

df_us <- na.omit(df_billionaires[c("country", "industries")]) %>%
    filter(country == "United States") %>%
    group_by(industries) %>%
    summarise(
        count = n()
    ) %>%
    arrange(desc(count))

df_china <- na.omit(df_billionaires[c("country", "industries")]) %>%
    filter(country == "China") %>%
    group_by(industries) %>%
    summarise(
        count = n()
    ) %>%
    arrange(desc(count))

df_india <- na.omit(df_billionaires[c("country", "industries")]) %>%
    filter(country == "India") %>%
    group_by(industries) %>%
    summarise(
        count = n()
    ) %>%
    arrange(desc(count))

# Dataframe for selfMade-country plotting
df_inherit_country <- filter(df_billionaires[c("country", "selfMade")], country != "") %>%
    na.omit() %>%
    group_by(country, selfMade) %>%
    summarise(count = n()) %>%
    arrange(selfMade, desc(count))

# delete all entries which are not in the list of top 10 inherited countries
df_inherit_country <- df_inherit_country[df_inherit_country$country %in% df_inherit_country[1:10, ]$country, ] %>%
    arrange(country, desc(count))


# use df_billionaires and corellate selfMade with some columns
columns <- c("gender", "category", "birthYear", "birthMonth", "age", "finalWorth", "country", "city", "state", "selfMade")

df_temp <- na.omit(df_billionaires[columns])

# convert columns to factor
df_temp <- as.data.frame(lapply(df_temp, as.factor))

# conver all to numeric
df_temp <- as.data.frame(lapply(df_temp, as.numeric))

# corellate selfMade attribute with everything
self_made_corr <- cor(df_temp)

# Print out the correlations of selfMade
self_made_corr <- self_made_corr["selfMade", ]

# Drop selfMade
self_made_corr <- self_made_corr[-which(names(self_made_corr) == "selfMade")]

The relationship between age and becoming a billionaire.

# Remove NA values
df_temp <- na.omit(df_billionaires)
# Filter for self-made billionaires and find the minimum age
youngest_age <- min(df_temp[df_temp$selfMade == TRUE, ]$age)

# Filter out rows with missing values in finalWorth or age
df_final_worth_below_25b <- subset(df_billionaires, !is.na(finalWorth) & !is.na(age) & finalWorth < 25000)

# Count the number of self-made billionaires
self_made_count <- sum(df_billionaires$selfMade == TRUE)

# Create age groups (you can adjust the breaks and labels accordingly)
age_breaks <- c(0, 20, 30, 40, 50, 60, 70, 80, 90, Inf)
age_labels <- c("<20", "20-29", "30-39", "40-49", "50-59", "60-69", "70-79", "80-89", "90+")

df_age_groups <- df_billionaires
df_age_groups$age_group <- cut(df_age_groups$age, breaks = age_breaks, labels = age_labels, right = FALSE)

# Create a summary table of the proportion of self-made billionaires in each age group
summary_table <- table(df_age_groups$age_group, df_age_groups$selfMade)
prop_table <- prop.table(summary_table, margin = 1)

Countries with lower tax rates have more billionaires.

# Get the countries with the lowest tax rates
lowest_tax <- df_billionaires[c("total_tax_rate_country", "country")] %>%
    # Remove NA values
    na.omit() %>%
    # Group by country
    group_by(country) %>%
    # Count billionaires per country
    summarise(
        total_tax_rate_country = first(total_tax_rate_country),
        count = n()
    ) %>%
    # Filter Argentina, because tax rate of 106,3% is not realistic.
    filter(country != "Argentina") %>%
    # Sort by total_tax_rate_country
    arrange(total_tax_rate_country)

highest_tax <- arrange(lowest_tax, desc(total_tax_rate_country))

Countries with better educational attainment have more billionaires.

# Get the countries with the best educational attainment
df_education <- df_billionaires[c("gross_primary_education_enrollment_country", "gross_tertiary_education_enrollment", "country")] %>%
    # Remove NA values
    na.omit() %>%
    # Group by country
    group_by(country) %>%
    # Count billionaires per country
    summarise(
        primary_education = first(gross_primary_education_enrollment_country),
        tertiary_education = first(gross_tertiary_education_enrollment),
        count = n()
        # Hier können Sie weitere Aggregationsfunktionen hinzufügen, falls benötigt
    ) %>%
    arrange(desc(primary_education))

df_temp_primary <- df_education

df_temp_tertiary <- arrange(df_education, desc(tertiary_education))

lowest_primary <- arrange(df_temp_primary, primary_education)

lowest_tertiary <- arrange(df_temp_tertiary, tertiary_education)


education_corr_df <- df_education
# convert columns to factor
education_corr_df <- as.data.frame(lapply(education_corr_df, as.factor))

# conver all to numeric
education_corr_df <- as.data.frame(lapply(education_corr_df, as.numeric))

# calculate correlation between count of billionaires and primary/tertiary education
education_corr <- cor(education_corr_df)

4. Appropriate visualization and aggregation of the data

Summarize the data in a suitable form to answer your formulated question. summarized. You should also use suitable visualizations of the transformed and/or aggregated data to support or illustrate your statements accordingly.

You can also use suitable statistical methods or modeling here if they help you with your research question.

The distribution of billionaire wealth is highly concentrated among a small number of individuals.

We think that only a small amount of individuals own a huge part of the entire wealth of all billionaires. The same is already proven for the entire world population: “half of the world’s net wealth belongs to the top 1%, top 10% of adults hold 85%, while the bottom 90% hold the remaining 15% of the world’s total wealth, top 30% of adults hold 97% of the total wealth”.

For that, we’ll look at the wealth distribution between the billionaires in this dataset:

The plot shows, that the lower the finalWorth is, the higher the frequency gets. This means that most billionaires are barely above the threshold of 1 billion usd. Only a few billionaires have a higher finalWorth than 25000 million usd.

How many billionaires own more than 80% of the total wealth?

Due to the global wealth being around $454.4 trillion in 2022. We cannot really calculate the amount of people that own 80% of the global wealth. But we can calculate the amount of billionaires that own more than 80% of the totalWealth of all billionaires:

## All billionaires wealth combined sums up to 12206800 million usd.
## The entire wealth of all billionaires is just 2.686356 % of the global wealth.
## Number of billionaires owning more than 80% of total wealth of billionaires: 1157

The richest 1157 billionaires own 80% of the combined wealth of all billionaires. Assuming this dataset is complete, there are 2640 billionaires.

Let’s look at how many billionaires own half the entire wealth of all billionaires:

## Number of billionaires owning more than 50% of total wealth of billionaires: 302

Only 302 of all 2640 billionaires own more than 50% of the total wealth. This proves that the tendency stays the same for the richest interval of wealth (billionaires) compared to the entire scope.

There is a strong correlation between the industries in which billionaires make their fortunes and the overall GDP of their countries.

To check if there is a correlation, lets compare the industries with the gdp:

On this plot you cannot really see a pattern. Lets zoom in on billionaires below $25 billion:

There is no obvious pattern or correlation.

Which industry is the most profitable?

To calculate the most profitable industry, we can calculate the average wealth for each industry and compare them:

## The most profitable industry is: Automotive

This bar chart shows that the industries don’t really differ in being the most profitable. Also the results are inaccurate, due to vague classifications of their industries.

The following bar chart shows the wealth distribution over all industries given in the data set. It also differentiates between self-made wealth and inherited wealth. I also removed the top billionaires above $25 billion.

Billionaires working in Energy, Telecom, Gambling & Casinos, Sports, Service and Construction & Engineering are mostly self-made. Industries like Finance & Investment and Media & Entertainment are dominated by billionaires that inherited their wealth. Overall the Technology and Manufacturing industry has a lot of billionaires below $25 billion. We should also mention that this data is probably not 100% accurate due to the rough classification of the industry of each billionaire.

What is the relationship between the distribution of billionaire wealth and the economic strength of a country, measured by GDP, tax revenue and population?

First, let’s look at the relationship between the GDP and the billionaires wealth:

There is no obvious correlation or trend. Now let’s look at the relationship between tax revenue and billionaires wealth:

This plot shows that there is no obvious correlation or trend between a countries tax revenue and the billionaires wealth. Last but not least, let’s look at the relationship between a countires population and the billionaires wealth:

Again, the plot shows no obvious correlation or trend. All these plots are biased by the amount of billionaires of each country. You can always identify the usa, france and china. The differences between those countries amost always are responsible for the fact that you cannot identify a correlation or trend.

The geographic distribution of billionaires across countries reflects the global economic landscape and the opportunities for wealth creation in different regions.

Is there a geographical pattern to the distribution of billionaires across countries?
If so, what factors contribute to this distribution?

Let’s first examine the number of billionaires per country.

The five countries with the most billionaires are:

## United States : 754 
## China : 523 
## India : 157 
## Germany : 102 
## United Kingdom : 82

What we can observe is that the highest number of billionaires resides in the USA, China, and India.
This is unsurprising, given that these countries possess the highest economic power.

Now, let’s turn our attention to the total wealth of billionaires per country.

This graph appears almost identical to the previous one. The most noteworthy observation is that the greatest wealth is concentrated in the USA and China. This might be attributed to the fact that the USA boasts the highest number of billionaires at 754, and China’s economy has recently transformed from a predominantly agrarian and impoverished nation to the world’s second-largest economy, fueled by market reforms and globalization.

Next, we can gain an overview of the industries in which billionaires are involved per country. Perhaps we can identify a pattern where the industry is closely tied to the country.
Let’s begin by examining the industries in the USA. The majority of billionaires in the US are in the finance and investment industry, followed by the technology and food & beverage industry. In China, the most billionaires are in the manufacturing industry, followed by the technology and healthcare industry. Like in China, the majority of billionaires in India are in the manufacturing industry. The second-largest group of billionaires in India is in the healthcare sector.

If we delve into the manufacturing industry in both China and India, we discover that China holds the title of the world’s largest manufacturing economy and exporter of goods, as mentioned earlier. India’s manufacturing industry has been experiencing rapid growth in recent years. This growth can be attributed to the comparatively low labor costs in both India and China.

However, previous analyses of GDP and industrial sectors have shown that there is no correlation between the sector/GDP and being a billionaire.

The only potentially existing geographical connection is that approximately 18% of the world’s population resides in both China and India. This is the only pattern that could explain the distribution of billionaires across these countries, suggesting that where more people live, there is also a greater potential for individuals to become billionaires.

In which countries do most billionaires inherit their wealth and why?

The country with the most billionaires also tops the list for inherited billionaires, reflecting a correlation between a higher billionaire count and inheritance opportunities. China, ranking second in billionaires globally, exhibits the lowest number of inherited billionaires on the graph, indicating a distinctive wealth distribution pattern. This could be due to fast economic changes and the socialist influences discouraging wealth inheritance.

To check further why billionaires inherit their wealth, we examine if there is a mathematical correlation between the ‘self-made’ attribute and other attributes. 

##       gender     category    birthYear   birthMonth          age   finalWorth      country         city        state 
##  0.324332535  0.194644898  0.051732894  0.001023638 -0.050433736 -0.045253107 -0.042652994  0.039837195 -0.012873595

The highest existing correlation is between gender and the self-made status, with a value of 0.32. However, this is likely because it was historically more challenging for women to establish businesses and accumulate wealth.

The second-highest value is the linear correlation between income category and the self-made status, with a value of 0.19. Both of these mentioned values, however, are too low to truly confirm a correlation between the attributes. This holds true for the other attributes in the graph as well.

The relationship between age and becoming a billionaire.

This plot gives on overview on the generated or inherited wealth by each billionaire indexed by their age. The x-axis demonstrates the age from 1 to 100+ and the y-axis demonstrates the wealth. Blue dots indicate a self-made wealth, while red dots indicate inherited wealth.

Let’s also look at the youngest self-made billionaire:

## The age of the youngest self-made billionaire is 28 years.

This indicates that young billionaires below the age of 28 most definitely inherited their wealth. The youngest self-made billionaire is 28 years old. As the age increases, there is no obvious pattern. The only thing worth mentioning is that the richest billionaires are mostly self-made. This probably results out of the fact of inflation and the growing gap between rich and poor. Let’s zoom in on billionaires with a wealth below $25 billion:

This also shows no obvious pattern.

Let’s also look at a bar chart showing the amount of billionaires for each age:

Here you can see that self-made billionaires are generally younger than billionaires who inherited their wealth. You can also see a lot of self-made billionaires who are about 58 years old. This may indicate that being born about 60 years ago is a good time to get rich.

How many billionaires are self-made, and are younger billionaires more likely to be self-made?

First let’s look at how many billionaires are self-made:

## Number of self-made billionaires:  1812 /2640

Out of the 2,640 billionaires worldwide, 1,812 are considered self-made, meaning they accumulated their wealth through entrepreneurship and business endeavors rather than inheriting it. This trend can be attributed to the increasing opportunities for innovation and entrepreneurship in the global economy, fostering a conducive environment for individuals to create and grow their own businesses. Factors such as technological advancements, globalization, and access to capital have empowered individuals to pursue entrepreneurial ventures, leading to a significant number of self-made billionaires. Additionally, the rise of industries like technology has provided platforms for innovative minds to disrupt traditional business models and amass substantial fortunes. Are younger billionaires more likely to be self-made?

No, young billionaires are not more likely to be self-made. Between the age of 30 and 90, most billionaires are self-made.

Countries with lower tax rates have more billionaires.

Which countries have the lowest tax rates and how many billionaires do they have?

First of all we wanted to find out which countries have the lowest tax rates.
We also take a look at the countries with the highest tax rates to compare these with each other. Georgia has the lowest tax rate at 9.9%. There is only one billionaire in Georgia. The first graph depicts a total of 155 billionaires. The country with the highest tax rate is Colombia, with a rate of 71.2%. Colombia also has only one billionaire. The second graph shows a total of 1019 billionaires.

When comparing these plots, we can observe that more billionaires live in countries with higher tax rates. In this plot, it is notably influenced by China; however, even when excluding China from consideration, the graph with higher tax rates still accumulates more billionaires than the one with lower tax rates.

This observation, though, is not representative enough to make a statement about whether countries with higher tax rates have more billionaires. Further studies would be needed to draw a conclusive conclusion.

Countries with better educational attainment have more billionaires.

Let us first get an overview of the amount of billionaires .

## Amount of billionaires in the first five countries with the best primary education:
## Nepal : 1 
## Sweden : 26 
## Brazil : 44 
## Colombia : 1 
## Morocco : 2
## Amount of billionaires in the first five countries with the best tertiary education:
## Greece : 3 
## Australia : 43 
## South Korea : 29 
## Argentina : 4 
## Spain : 25

Lets do the same for the countries with the lowest educational attainment:

## Amount of billionaires in the first five countries with the lowest primary educational enrollment:
## Nigeria : 3 
## Romania : 3 
## Armenia : 1 
## Turkey : 25 
## Tanzania : 1
## Amount of billionaires in the first five countries with the lowest tertiary educational enrollment:
## Tanzania : 1 
## Uzbekistan : 1 
## Nigeria : 3 
## Nepal : 1 
## Cambodia : 1

If we compare those we can clearly see, that there are more billionaires in countries with better educational attainment.
This could be an indicator that the educational attainment is a factor for becoming a billionaire. 

Is there a correlation between educational attainment and the number of billionaires in a country?

Now lets calculate if there is a correlation between the amount of billionaires and the primary/tertiary education.

## Correlation: billionaires - primary education: 0.03536343 
## Correlation: billionaires - tertiary education: 0.2497601

The correlation between the number of billionaires and primary education is 0.035 (3.5%). The correlation for tertiary education is 0.249 (24.9%).

From this, we can deduce that there is no connection between primary education and the number of billionaires.
However, tertiary education shows a slight linear relationship with the number of billionaires.
This could make sense since tertiary education represents the highest level of education.

5. Summary and conclusion

Summarize your research question and your findings from your analysis here.  Are your findings what you expected? Why or why not?

This analysis aimed to examine patterns and correlations related to billionaire wealth, demographics, geography, industries, and national economic factors.
The key questions explored were:

The main findings were:

Overall, the findings show some interesting patterns but do not strongly validate many of the initial hypotheses. The limitations of the data likely prevented finding stronger correlations between industries, GDP, geography and other factors. More extensive data would be needed to draw firmer conclusions. The analysis provides a useful starting point for understanding the demographics and distribution of billionaire wealth.